Elasticsearch
Elasticsearch is a distributed search and analytics engine designed for horizontal scalability and near real-time search. It’s built on top of Apache Lucene and is used for a variety of use cases such as log and event data analysis, full-text search, and monitoring systems.
Why Choose Elasticsearch?
- High performance: Elasticsearch provides fast search responses, even for large datasets, due to its distributed nature.
- Scalability: The ability to scale horizontally across multiple nodes makes it suitable for handling increasing volumes of data.
- Flexible queries: Elasticsearch supports complex queries and full-text search with a powerful query DSL.
- Integration: Easily integrates with the ELK stack (Elasticsearch, Logstash, and Kibana) for powerful logging and visualization.
Configuration Tips:
- Shards and replicas: Properly configure the number of primary shards and replicas for optimal performance and redundancy.
- Index management: Use time-based indices for logs or large datasets and apply index lifecycle management (ILM) for automatic index rollover.
- Resource allocation: Allocate sufficient JVM heap size (50% of total memory) and avoid excessive swapping for better performance.
- Monitoring: Enable monitoring with Kibana to track node health, resource usage, and query performance.
Example:
- Search scenario: If you have a product catalog, Elasticsearch allows users to search by product name, description, or category with fuzzy matching, autocomplete, and filtering options.
- Log analysis: For a system log monitoring pipeline, use Logstash or Beats to send logs to Elasticsearch, where they are indexed and can be visualized through Kibana dashboards.